We optimize parameters of each model separately from one another by minimizing the corresponding loss objective (Eq. REF , REF ). We reserve 20% of training data for validation and 1000 data points for testing. For each model, AdamW [1]} optimizer is used with initial learning rate \(10^{-3}\)  and weight decay \(10^{-2}\) . CVAEs are trained for 30 epochs with early stopping and learning rate decaying by the factor of \(0.1\)  every 10 epochs. We train normalizing flows for 100 epochs with early stopping and decreasing learning rate by \(0.1\)  every 30 epochs. For each model, the training was performed in mini-batches of size 32. We use NVIDIA Tesla V100 GPU from the Hemera HPC system of HZDR.
